A large body of
human-computer interaction research has focused on developing metaphors and tools that allow users to effectively issue commands and directly manipulate informational objects.
However, with the advancement of computational techniques such
as machine learning, we now have the unprecedented ability to
embed 'smarts' that allow machines to assist
users in completing their tasks. We believe that trying to
fully automate tasks is extremely difficult and even
undesirable, but instead there exists a computational design
methodology which allows us to gracefully combine automated services with direct user manipulation.
Network alarm triage refers to grouping and prioritizing a
stream of low-level device health information to help
operators find and fix problems. Today, this process tends
to be largely manual because existing tools cannot easily
evolve with the network.
CueT is a system that
uses interactive machine learning to learn from the triaging
decisions of operators. It then uses that learning in novel
visualizations to help them quickly and accurately triage
alarms. Unlike prior interactive machine learning systems,
CueT handles a highly dynamic environment where the
groups of interest are not known a-priori and evolve
ManiMatrix is a system that provides
controls and visualizations that enable system builders to
refine the behavior of classification systems in an intuitive
manner. With ManiMatrix, users directly refine parameters
of a confusion matrix via an interactive cycle of reclassification and visualization.
Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative.
In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components.
We designed and developed a new interactive visualization system that presents a graphical view of confusion matrices to help users under-stand relative merits of various classifiers.
CueFlik: Interactive Concept Learning in Image Search
Popular image search engines have begun to provide tags based on
simple characteristics of images (such as tags for black and white images or
images that contain a face), but such approaches are limited by the fact that it
is unclear what tags end users want to be able to use in examining image search
results. CueFlik is an image search application that allows
end users to quickly create (and reuse) their own rules for re-ranking images based on their
CueTIP: Mixed-Initiative Handwriting Recognition
Handwritten input is inherently ambiguous, and recognition
systems will always make errors. Unfortunately, work on error recovery mechanisms has
mainly focused on interface innovations that allow users to manually transform the erroneous
recognition result into the intended one. In our work, we propose a mixed-initiative approach
to error correction. CueTIP is a novel correction interface that takes advantage of the
recognizer to continually evolve its results using the additional information from user corrections.
This significantly reduces the number of actions required to reach the intended result.